Goldman Sachs Raises EQIX Target Price to $1015, Bullish on AI Inference Demand but Q1 EBITDA Below Expectations Due to xScale Timing Delays

Overview

This report reviews Equinix (EQIX)'s performance in the first quarter of 2026. Although revenue and EBITDA fell short of expectations due to timing issues with the Hampton xScale project, the company slightly raised its full-year financial guidance for 2026. Goldman Sachs believes Equinix is well-positioned for growth in AI inference investments, particularly given the trend toward hybrid multi-cloud adoption driven by data sovereignty and latency requirements. As a result, analysts raised their earnings forecasts and target price for the next three years, reflecting stronger confidence in the growth of AI inference demand.

Core views

In terms of performance, Equinix's adjusted EBITDA for the first quarter was $1.245 billion, lower than Goldman Sachs' forecast of $1.301 billion and the market consensus of $1.299 billion. This deviation was mainly due to weaker-than-expected revenue, where non-recurring income was affected by timing issues with the Hampton xScale project, and recurring revenue was also slightly below expectations. The company noted that if the $80 million negative impact from Hampton timing were excluded, the first-quarter results would have exceeded expectations, which explains the slight adjustment to full-year guidance. Regarding guidance and forecast adjustments, Equinix raised its full-year total revenue guidance for 2026 to $10.144–$10.244 billion (previously $10.123–$10.223 billion), adjusted EBITDA guidance to $5.165–$5.245 billion (previously $5.141–$5.221 billion), and AFFO per share guidance to $4.231–$4.311 (previously $4.193–$4.274). Based on this, Goldman Sachs raised its 2026/27/28 EBITDA and AFFO per-share forecasts by an average of 3%, reflecting both the upward revision of quarterly outlooks and full-year guidance. In terms of valuation and views, Goldman Sachs significantly raised the 12-month target price from $894 to $1,015. This increase was driven not only by higher earnings forecasts but also by an expansion of the valuation multiple: the target multiple rose from 19.5x to 21.0x (based on NTM+1Y P/AFFO). Analysts emphasized that although the stock price has already risen 42% this year and annual bookings have declined from record highs, market enthusiasm for the substantial upward revision may be cooling down. However, in the long term, Equinix benefits from the growth in AI inference investment, as agent workloads—driven by data sovereignty and latency requirements—are shifting closer to the edge rather than relying entirely on the cloud, which aligns perfectly with Equinix's Colocation platform positioning. In terms of risks, upside risks include tight supply in the data center market, strong demand in Metro regions, and increased demand for low-density infrastructure driven by innovations in chip and power efficiency; downside risks include oversupply, weak demand from key customers, obsolescence risk for existing data centers, and intensifying price competition among outsourced data center providers.

Analysis framework

The report adopted a typical 'performance review - guidance revision - earnings forecast reevaluation - valuation restructuring' analytical framework. First, it compared actual financial data (such as EBITDA and revenue) with sell-side estimates (Consensus/GS Estimate) to identify short-term deviations and their drivers (here, the absence of non-recurring income caused by specific project timing). Second, it analyzed management's slight adjustment to full-year guidance to determine the direction of future cash flow revisions. Finally, combining macro industry trends (the shift of AI inference toward the edge, data sovereignty requirements), it reassessed the company's long-term growth potential, adjusting not only earnings figures (EPS/EBITDA) but also valuation multiples (Target Multiple), ultimately arriving at a new target price. This analytical approach balances short-term performance fluctuations with long-term structural opportunities.

Methodology notes

• Valuation MethodP/AFFO Valuation

  A valuation method used for Real Estate Investment Trusts (REITs) or capital-intensive industries, using Adjusted Funds From Operations (AFFO) instead of GAAP earnings per share.

  The report mentioned that the target price was based on '21.0x NTM+1Y P/AFFO.' This is because REITs need to deduct non-cash expenses such as depreciation and amortization, making AFFO a better reflection of their true sustainable cash flow capacity. By increasing this multiple (from 19.5x to 21.0x), analysts are recognizing the premium attached to the certainty of growth driven by AI demand.

• Industry/Industrial Analysis FrameworkSupply-demand framework

  Judging industry prosperity by analyzing changes in the supply side (such as data center construction cycles and capacity release) and the demand side (such as AI and cloud computing demand).

  The report explicitly mentioned 'tight incremental supply' as an upside risk and 'oversupply' as a downside risk in its risk warnings, while emphasizing the growth in AI inference demand in its core view. This indicates that analysts are using the supply-demand framework to assess the balance of the data center market, supporting their bullish logic.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• Equinix Inc. (EQIX.US)

  Direct beneficiary. As a leading global Colocation provider, its geographic advantages enable it to handle AI inference workloads that require proximity to the edge due to data sovereignty and latency sensitivity.

  Strengths

  At the heart of the growth in AI inference investment; Colocation platform suitable for hybrid multi-cloud and data sovereignty needs; Full-year guidance raise shows fundamental resilience.

  Weaknesses

  Q1 revenue and EBITDA below expectations due to specific project timing delays; stock price up 42% this year, valuations not cheap; annual bookings decline from peak levels.

  Comparison

  The report placed it within the coverage universe including Digital Realty Trust Inc., but did not provide detailed horizontal comparisons.

  Risks

  Oversupply in the data center market; weak demand from key customers; obsolescence risk for existing data centers; intensified price competition among outsourced data center providers.

Key data

• Q1 2026 Adjusted EBITDA$1,245 mnActual figure, below GS estimate of $1,301 mn
• Q1 2026 Total Revenue$2,444 mnActual figure, below GS estimate of $2,519 mn
• 2026E Total Revenue Guidance$10,144-$10,244 mnSlightly raised from previous guidance ($10,123-10,223 mn)
• 2026E Adjusted EBITDA Guidance$5,165-$5,245 mnSlightly raised from previous guidance ($5,141-5,221 mn)
• New Target Price$1,015Significantly raised from $894 previously, corresponding to 21.0x NTM+1Y P/AFFO
• Annualized Bookings (Q1)$378 mnReached a Q1 record, but down from $474 mn in Q4

Impact & implications

For investors, although short-term performance fell short of expectations due to one-time timing factors, the upward revision of full-year guidance and the significant increase in the target price indicate that the long-term growth logic (AI inference) remains strong. The expansion of the valuation multiple suggests that the market is willing to pay a premium for certain AI-related demand. However, since the stock price has already risen substantially this year (+42%) and bookings have peaked month-on-month, there could be profit-taking pressure or expectation management challenges in the short term; attention should be paid to the sustainability of AI demand in subsequent quarters.

Risks

• Oversupply in the data center market
• Weak demand from key customers
• Obsolescence risk for existing data centers
• Intensified price competition among outsourced data center providers

What to watch

• Sustainability and implementation speed of AI inference demand
• Changes in supply-demand balance in the data center market
• Specific demand dynamics in Equinix's Metro regions
• Impact of chip and power efficiency innovations on infrastructure density